Understand
Large language models (LLMs) exhibit impressive capabilities in generation tasks but are prone to producing harmful, misleading, or biased content, posing significant ethical and safety concerns.
- To mitigate such risks, representation engineering, which steer model behavior toward desired attributes by injecting carefully designed steering vectors into LLM's representations at inference time, has emerged as a promising alternative to fine-tuning approaches.
- However, due to the semantically entangled nature of LLM's representation, existing representation engineering methods still suffer from several limitations: limited fine-grained controllability, content quality degradation, and conflict in multi-attribute control.
- To overcome these challenges, we propose Sparse Representation Steering (SRS), a novel framework that achieves fine-grained and interpretable control over LLM behavior by first disentangling internal activations into a sparse, semantically meaningful representation space, and then selectively steering relevant dimensions.